/**
 * Copyright 2012 Brigham Young University
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package edu.byu.nlp.cluster.mom;

import edu.byu.nlp.util.DoubleArrays;

/**
 * Note, the parameters alpha and beta are interpreted differently in this case,
 * namely, that they are equivalent to one more than their specified value. (The mode of a Dirichlet distribution
 * is proportional to the parameter less one).
 */
class ModeAssigner implements Assigner {

	/** {@inheritDoc} */
	@Override
	public void assignThetaInPlace(double[] alphaStar) {
		// TODO : make convenience methods for these things
		DoubleArrays.normalizeAndLogToSelf(alphaStar);
	}

	/** {@inheritDoc} */
	@Override
	public void assignPhiInPlace(double[][] betaStar) {
		for (int k = 0; k < betaStar.length; k++) {
			DoubleArrays.normalizeAndLogToSelf(betaStar[k]);
		}
	}

	/** {@inheritDoc} */
	@Override
	public int assignY(double[] completeConditional) {
		return DoubleArrays.argMax(completeConditional);
	}
	
}